Large-scale graphs, such as social networks, are highly dynamic and the ability to mine them in real time can enable better services, such as timely friend and content recommendation on social networks. In this paper, we present the basic concept behind our real-time graph mining system. We show how the technique of memoization can be used to transparently compute graph algorithms in an incremental fashion, speeding up the computation when the input graph changes.
Our paper at the CICM/CloudDB’12 workshop presents an initial study and some early results on our approach. Find a copy here.